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The temperature of surface and epilimnetic waters, closely related to regional air temperatures, responds quickly and directly to climatic changes. As a result, lake surface temperature (LSWT) can be considered an effective indicator of climate change. In this study, we reconstructed and investigated historical and future LSWT across different scenarios for over 80 major lakes in mainland Southeast Asia (SEA), an ecologically diverse region vulnerable to climate impacts. Five different predicting models, incorporating statistical, machine and deep learning approaches, were trained and validated using ERA5 and CHIRPS climatic feature datasets as predictors and 8-day MODIS-derived LSWT from 2000 to 2020 as reference dataset. Best performing model was then applied to predict both historical (1986-2020) and future (2020-2100) LSWT for SEA lakes, utilizing downscaled climatic CORDEX-SEA feature data and multiple Representative Concentration Pathway (RCP). The analysis uncovered historical and future thermal dynamics and long-term trends for both daytime and nighttime LSWT. Among 5 models, XGboost results the most performant (NSE 0.85, RMSE 1.14 °C, MAE 0.69 °C, MBE -0.08 °C) and it has been used for historical reconstruction and future LSWT prediction. The historical analysis revealed a warming trend in SEA lakes, with daytime LSWT increasing at a rate of +0.18 °C/decade and nighttime LSWT at +0.13 °C/decade over the past three decades. These trends appeared of smaller magnitude compared to global estimates of LSWT change rates and less pronounced than concurrent air temperature and LSWT increases in neighbouring regions. Projections under various RCP scenarios indicated continued LSWT warming. Daytime LSWT is projected to increase at varying rates per decade: +0.03 °C under RCP2.6, +0.14 °C under RCP4.5, and +0.29 °C under RCP8.5. Similarly, nighttime LSWT projections under these scenarios are: +0.03 °C, +0.10 °C, and +0.16 °C per decade, respectively. The most optimistic scenario predicted marginal increases of +0.38 °C on average, while the most pessimistic scenario indicated an average LSWT increase of +2.29 °C by end of the century. This study highlights the relevance of LSWT as a climate change indicator in major SEA's freshwater ecosystems. The integration of satellite-derived LSWT, historical and projected climate data into data-driven modelling has enabled new and a more nuanced understanding of LSWT dynamics in relation to climate throughout the entire SEA region., Competing Interests: Declaration of competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

This study assesses the cumulative impact of climate change and reservoir operation on flow regime and fisheries in the Sekong River Basin. Ensemble of five selected Regional Climate Models (RCMs) were used to project the future climate under RCP4.5 and RCP8.5 scenarios. The projected future climate was used to simulate the future hydrology using the SWAT model while HEC-ResSim was utilized for reservoir simulation. Finally fish-flow relationship was developed to estimate the fish catch and productivity in future. Upon investigation we found that, Sekong River Basin is likely grow warmer and drier in future under climate change. The basin is expected to face 1.3-3.6 °C rise in mean annual temperature and receive 0-6% less annual rainfall in future. The wet season in the basin is anticipated to be drier (0% to -6%) while the dry season rainfall shows no particular trend (-3%-10%). Such a change in climate is likely to alter the mean annual flow in future between -3 and 5% at Attapeu, -6 to 2% at Ban Veunkhane, Lao PDR, and -7 to 1% at Siempang, Cambodia (basin outlet). Under climate change, we expect decrement in minimum flow but increment in the maximum flow while opposite is anticipated under reservoir operation. Operation of Xekaman 1 and Sekong 4A are likely to increase the minimum flow at river outlet by 32-59% and 13-18% respectively whereas maximum flow is expected to decrease by 28-5%. In addition, climate change is likely to have crucial impact on fisheries with up to 19% and 12% reduction in fish catches and fish productivity respectively. However, reservoirs tend to have negligible impact on fisheries., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 Elsevier Inc. All rights reserved.)

Evapotranspiration (ET) is a critical variable in the world's water cycle, and plays a significant role in estimating the impact of environmental change on the regional hydrothermal cycle. Moreover, as an essential of eco-hydrological processes, changes in ET may exceptionally impact the local climate and provide indicative information on the eco-system's functioning. The Hailar River Basin (HRB), located in northern China, is one of the most sensitive areas to climate warming. Under the influence of climate change in recent years, the vegetation dynamics of the basin have been significant and have had profound effects on the regional water cycle conditions and hydrological processes. The HRB is located in a semiarid region and ET is the main mode of water consumption. The ET response to climate change and vegetation dynamics is the focus of research on ecohydrological processes in this basin. In this study, a distributed hydrological model, the BTOPMC model, is used to evaluate the actual ET in the HRB from 1981 to 2020, based on in situ meteorological data as well as LAI data obtained by satellite remote sensing. The seasonal, interannual and spatial dynamics of ET were characterized. The contribution of meteorological factors to ET was calculated by sensitivity analysis and multiple linear regression analysis, and the predominant elements influencing the difference in ET in the HRB were also discussed. The results show that: (1) estimated ET values can clarify over 85% of the seasonal variation in the observed values (R 2 = 0.79, P < 0.001; R 2 = 0.84, P < 0.001), which demonstrates that the model has a high precision. (2) Over the past 40 years, the annual ET has shown a clear increasing trend and a large spatial heterogeneity in its spatial distribution, which is consistent with the trend of vegetation. It mainly shows that the eastern forest area is larger than the central forest-grass transition area and the western meadow steppe area. (3) Sensitivity and influential factor contribution analyses show that the main factor driving interannual variability in ET is climate warming, followed by precipitation. At the same time, vegetation dynamics also play a crucial role in ET, especially in areas with different vegetation types and high coverage, while climatic factors also have a strong influence on ET indirectly through vegetation. Due to its special geographic location, the HRB is more sensitive to global climate change and is a typical ecologically fragile area. Therefore, this study has important scientific value and social significance for maintaining ecological security and the sustainable use of water resources., (Copyright © 2022 Elsevier Inc. All rights reserved.)

Many lakes in semiarid regions around the world rely on environmental water allocation to maintain the health of the lake ecosystem. However, under changing environments, the competition for water resources between human society and natural ecosystems has intensified. How to manage environmental water allocation more reasonably and precisely has become an important issue. The largest lake on the North China Plain, Baiyangdian Lake (BYDL), is a typical lake facing such challenges. To provide feasible strategies for sustainable water allocation to BYDL, with the proper parameterization of hydrological processes, this study developed a 10-day temporal scale lake water level prediction model to quantify how environmental water allocation regulates the BYDL water level under different hydroclimatic conditions. Evaluation of model performance revealed that environmental water allocation rather than natural climatic periodicity dominates the variation in the BYDL water level. The model structure could be further improved with consideration of more detailed observations of both the surface runoff entering BYDL and the water area beneath the canopy of the reeds in BYDL. Analysis of 72 model simulation scenarios indicated that water allocations from multiple sources are indispensable and that the water resources that guarantee maintaining the BYDL water level within the ecologically suitable range vary substantially under different hydroclimatic conditions. More elaborate allocation plans are required both to improve the water quality and health of the aquatic ecosystem of BYDL and to reduce the risk of flooding. The findings from this study are valuable for guiding the implementation of environmental water allocations to lakes in semiarid regions worldwide., (Copyright © 2022. Published by Elsevier Inc.)

Lakes in arid/semiarid regions face problems of insufficient inflow and degradation of water quality, which threaten the health of the lake ecosystem. Baiyangdian Lake (BYDL), the largest lake in the North China Plain, is confronted with such challenges. The objective of this study was to improve understanding of how changes in water level influence water quality in the BYDL at different temporal scales, especially related to implementations of intermittent environmental water allocation activities in the past two decades, by using data on monthly lake water level, climate factors of precipitation and temperature, and lake water quality. The Mann-Kendall method and continuous wavelet analysis revealed that the lake water level shows a significant decreasing trend after 1967, and the period of 16-year was identified as the principal period for 1950-2018. Based on cross-wavelet transform and wavelet coherence analysis, the periodic agreement and coherence between water level and climatic factors decreased after 1997, when environmental water allocations started, indicating that the influences of climatic factors, i.e., precipitation and temperature, became weak. By utilizing the cross-wavelet transform and wavelet coherence analysis methods, the relationships between lake water level and water quality parameters of chemical oxygen demand, ammonia nitrogen, total nitrogen, and total phosphorus were investigated. We found that the change in source and amount of environmental water allocation is one possible reason for the temporal evolution in joint variability between lake water level and water quality. Meanwhile, a dilution effect of freshwater allocated to BYDL was detected in the time-frequency domain. However, the result also indicates that the driving mechanism of water quality is complex due to the combined impacts of water allocation, nonpoint source pollution in the rainy season, and nutrient release from lake sediment. Our findings improve the general understanding of changes in water level in lakes located in arid and semiarid regions under climate change and intensive human activities, and also provide valuable knowledge for decision making in aquatic ecosystem restoration of BYDL and other similar lakes., (Copyright © 2022 Elsevier Inc. All rights reserved.)

Variations in vegetation are influenced by regional climate regimes and, in turn, control the water balance behavior in water-limited regions. Owing to its role in ecohydrological processes, vegetation is an essential link in modeling the relationships among climate conditions, vegetation patterns, and dynamic water balance behavior. However, previous ecohydrological models have been empirical and complex, without physically significant parameters. Here, we propose a novel ecohydrological model (a Budyko model-coupled vegetation model) that combines the impacts of climate change and vegetation variations, featuring simple and deterministic parameters. In addition to accounting for the fundamental water balance model and its factors, mean precipitation, potential evapotranspiration, runoff, and variations in water storage (δS), the model showed better performance when incorporating δS (RMSE = 2.72 mm yr -1 ) and its parameter ε -, which is mechanically and quantitively subject to the vegetation coverage (R 2  = 0.95, p < 0.01). This was estimated as a function of vegetation potential canopy conductance, mean rainstorm depth, mean time between storms, and potential rate of evapotranspiration in a semi-arid watershed with impulsive precipitation in China (R 2  = 0.80, p < 0.01). The model also found that vegetation growth was mainly controlled by soil water content and decoupled the impact of the total amount of precipitation on vegetation in the northeastern area of the watershed. Hence, our method presents a new tool for building an ecohydrological model that includes deterministic parameters of mechanical significance., (Copyright © 2022 Elsevier Inc. All rights reserved.)

Groundwater is an important source of water supply in the Tak Special Economic Zone of Thailand. However, groundwater is under stress from climate change, land use change, and an increase in abstraction, affecting the groundwater level and its sustainability. Therefore, this study analyses the impact of these combined stresses on groundwater resources in the near, mid, and far future. Three Global Climate Models are used to project the future climate under SSP2-4.5 and SSP5-8.5 scenarios. According to the results, both maximum and minimum temperatures are likely to show similar increasing trends for both scenarios, with a rise of approximately 1 (1.5), 2 (3), and 3 (5) °C expected for SSP2-4.5 (SSP5-8.5) in each consecutive period. Annual rainfall is expected to continually increase in the future, with around 1500-1600 mm in rainfall (11ꟷ5.43% higher). Land use change is predicted for two scenarios: business as usual (BU) and rapid urbanisation (RU). The forest area is expected to increase to 30% (35%) coverage in 2090 for BU (RU) while agriculture is likely to reduce to 60% (50%) with the urban area increasing to 2.4% (7%). Water demand is predicted to increase in all future scenarios. The SWAT model is used to project recharge, which is likely to increase by 10-20% over time. The highest increase is predicted in the far future under SSP2 and RU scenarios. MODFLOW was used to project future groundwater resources, but due to the lack of consistent data, the time scale is reduced to yearly simulation. The results reveal that the groundwater level is expected to increase in the central part (urban area) of the study area and decrease along the boundary (agricultural area) of the aquifer. This research can aid policymakers and decision-makers in understanding the impact of multiple stressors and formulating adaptation strategies to manage groundwater resources in special economic zones., (Copyright © 2022. Published by Elsevier Inc.)

The rapid pace of urbanization blended with climate change has significantly altered surface and groundwater flows. In the context of tourism-driven economic potential areas, these drivers have greater effects, including threatening groundwater availability. This study assessed the combined impacts of climate and land use changes on the groundwater recharge (GWR) in Siem Reap, Cambodia utilizing Phase Six of the Coupled Model Intercomparison Project (CMIP6) global climate models (GCMs), DynaCLUE land-use model, and Soil Water Assessment Tool (SWAT). Three climate models CanESM5, EC&#95;Earth3, and MIROC6, out of seven, best captured the observed data after performance evaluation through the entropy method, were bias-corrected linearly for two shared socioeconomic pathways (SSPs) - SSP2-4.5 and SSP5-8.5. The results indicate a general increase in precipitation under both SSPs, while the average annual maximum temperature is likely to increase by 0.024 °C/year and 0.049 °C/year under SSP2-4.5 and SSP5-8.5, respectively. A similar trend but relatively higher increase is expected for the minimum temperature. Furthermore, the historical land use change showed the expansion of urban settlement by 373% between 2004 and 2019 at the expense of forest and shrubland. Future land use projections from the DynaCLUE model show that the urban settlements in the study area are likely to expand, from their 2019 condition, by 55% in 2030, 209% in 2060, and 369% in 2090 under SSP2 and at double of these rates under SSP5 scenario. The GWR is expected to rise by 39-53% during the wet season and decrease by 13-29% during the dry season under both scenarios. Meanwhile, under constant land use, the GWR is likely to increase more compared to other scenarios, highlighting the importance of land use planning to policymakers and planners. Additionally, the study shall also be important to practitioners and researchers in understanding, planning, and evaluating the performance of multiple climate models in groundwater assessment., (Copyright © 2022 Elsevier Inc. All rights reserved.)

This study proposes a methodological framework to evaluate and rank climate models based on extreme climate indices of precipitation and temperature for impact studies in seven sectors: Cryosphere, Energy, Forestry/GHGs, Health, Agriculture & Food Security, Disaster Risk Reduction (flood and drought), and Water Resources & Hydrology. The ranking of the climate models is based on their performance in sector-relevant extreme climate indices. Extreme climate indices for observed and climate models' datasets for a historical period and overall performance statistics were used to create a payoff matrix. The payoff matrix then served as an input to a multi-criteria decision-making process to rank the climate models for each of the climate indices. The final sector-specific ranking was achieved by averaging the ranks obtained in the sector-relevant indices. The developed methodology is demonstrated with an application to the Songkhram River Basin (Thailand), a sub-basin of the Mekong. Eighteen CMIP6 GCMs are used for the proposed evaluation and ranking processes and four performance statistics were used. Weights to each of the four performance statistics were determined using the entropy method. Compromise programming was applied to rank the GCMs based on the distance technique. The results indicate that the six best performing models are different for different sectors, with the GFDL&#95;CM4 model common in all the seven sectors considered in the study. KACE1&#95;0&#95;G, GFDL&#95;ESM4, GFDL&#95;CM4, MRI&#95;ESM2&#95;0, and ACCESS&#95;ESM1&#95;5 models are the five top (ranked 1 to 5 respectively) performing models for the Water Resources & Hydrology sector. The developed framework is generic and can be applied to any region or basin; at the same time, it can also provide researchers and policymakers with specific information on best-performing models for particular sectors., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 Elsevier B.V. All rights reserved.)

Soil water is the dominant factor controlling evapotranspiration (ET) in arid and semi-arid regions. However, the widely used ET simulation models, such as the Shuttleworth-Wallace model (S-W model), are insufficient in simulating the direct influence of soil moisture (SM), especially in the root zone. Based on SM and ET field data, we found that the influence of SM on ET increased with soil depth in the grassland. Evaporation in the S-W model was optimized by SM at 0-5 cm as the root mean square error (RMSE) decreased from 1.4 to 0.17, while transpiration was optimized by SM at 10-20 cm as the RMSE decreased from 0.26 to 0.07. The modified S-W model incorporating SM was called the S-W-ϴ model. To up-scale application and to verify the accuracy of the S-W-ϴ model under watershed water balance, we replaced the ET simulation module based on the S-W model with our S-W-ϴ model and the Block-wise TOPMODEL with Muskingum-Cunge routing method (BTOPMC) model that we used as the basis of our simulation. The influence of SM was determined by the proportion of root biomass of different vegetation types at different depths, and each depth interval was assigned a weighting reflecting its degree of influence. The results showed that the S-W-ϴ model improved accuracy with all the modification schemes tested. The modification scheme determined by the vegetation root distribution pattern had the greatest effect, providing a 4% accuracy improvement. The modified ET and hydrological models have the potential to support water basin management to a greater extent., (Copyright © 2022 Elsevier Inc. All rights reserved.)

Climate change is projected to affect the hydrological cycles in China, while the effects are expected to vary spatiotemporally. Understanding the variations in water security conditions and their sensitivity to climatic variables is crucial for assessing regional ecosystem responses to climate change. In the present study, we estimated the water yield capacity, an important indicator of water security in North China (NC), at a spatial resolution of 1 km during the last two decades based on the Budyko framework and quantified the sensitivity of water yield change to climate change among different vegetation types. The results showed that the performances of the Budyko framework were reliable both at the pixel scale and across large watersheds. The annual water yield in North China was estimated to be 7.61 ± 2.67 ∗ 10 10  m 3 /yr, with an average mean water yield (MWY) of 49.51 ± 17.49 mm/yr. The spatial pattern of mean water yield change (MWYC) exhibited high heterogeneity; 46% of the study region was dominated by an increasing trend, while 9.84% was statistically significant (P < 0.05). Compared with temperature, the water yield capacity was more sensitive to precipitation variation. A consistent trend of variation was found in cropland between water yield and precipitation, while negative sensitivity coefficients were found in natural vegetation types. The variation in sensitivity coefficients (Swyp) in natural vegetation showed that in regions with a decrease in precipitation, the variation in water yield capacity also decreased, while in regions with an increase in precipitation from 0 to 8 mm/yr, the water yield capacity first decreased and then increased with precipitation. Our findings suggest that grass and shrubs would be more beneficial to regional water security in North China's revegetation, while afforestation would provide protection for the regional environment from extreme rainfall events., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2021 Elsevier B.V. All rights reserved.)

This study evaluates the ability of 21 Regional Climate Models (RCMs) from the Coordinated Regional Climate Downscaling Experiment (CORDEX) in simulating climate extremes in the fast growing Asian cities which are highly vulnerable to climate change. The three Asian cities have two different climate characteristics, namely Bangkok and its vicinity and Ho Chi Minh City in tropical climate region and Kathmandu in sub-tropical and temperate climate region. The RCMs were evaluated to simulate the six climate indices; Consecutive Dry Days (CDD), Simple Daily Intensity Index (SDII), Number of extremely heavy precipitation days (R50mm), Maximum 1-day precipitation amount (RX1day), Mean of daily maximum temperature (TX mean) and Mean of daily minimum temperature (TN mean). The performance indicators used were correlation coefficient, normalized root mean square deviation, absolute normalized root mean square deviation and average absolute relative deviation. The Entropy method was endorsed to acquire weights of these four indicators and weightage average techniques were used for ranking of 21 RCMs. The result demonstrated that the best model for one climate index is not the same best model for other climate indices. The 3 RCMs; WAS44&#95;SMHI&#95;RCA4&#95;IPSL&#95;CM5A&#95;MR, WAS44&#95;SMHI&#95;RCA4&#95;MIROC5, and WAS44&#95;IITM&#95;REGCM4-4&#95;CSIRO&#95;MK3-6-0 are the best performing RCMs for simulating future climate extremes in Bangkok and its vicinity, Ho Chi Minh city and Kathmandu valley, respectively. Therefore, they are recommended to use for climate change impact and adaptation studies in water resources management in the selected cities., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2021 Elsevier B.V. All rights reserved.)

Once a key factor behind Vietnam's successful Doi Moi (restoration) economic reforms, the rice-centered agriculture of the VMD is now confronted by the new pressure of climate change impacts, including the intensifying salinity intrusion (SI). The SI menace has partly triggered the delta-wide emergence of new adaptive livelihood models across the VMD, including the prawn rice rotational crop (PRRC) that is arguably the most prominent. Research on the SI-driving factors is rapidly increasing in numbers, yet little synthesis has been done. Likewise, several studies have investigated the economic benefits of PRRC; less emphasis has placed on environmental and societal aspects, hence the questionable sustainability. This study, therefore, contributes a composite literature review, targeting two SI-related aspects: (i) key factors driving the intensification of SI in recent years across the Mekong Delta, and (ii) current understanding of the sustainability of PRRC. Results from the first review assignment highlight the four key SI-driving factors: riverbed incision, land subsidence, upstream dams, and sea-level rise. Also remarked are the critical absence of studies addressing multiple drivers and the need for a decoupling model to quantify the relative importance of each factor to strategize the adaptive measures. For PRRC, we reveal that while economic benefits have been widely reported, potential negative impacts of this model related to environmental and social aspects are lacking. Therefore, while the lucrative prawn trade might financially benefit the farmers', the economic benefit is marred by the underlying negative environmental impacts and social inequalities, limiting overall sustainability. This study also provides a case study to notify the spatial-temporal trends of PRRC in the last three decades and evaluate the associated geographical and social factors. Kien Giang province was selected as the study site since it is the largest PRRCacross the VMD. The lessons from Kien Giang can also be applied to other transformative agricultural models in both Mekong Delta and other deltas worldwide., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2021 Elsevier B.V. All rights reserved.)

Groundwater contributes to the socioeconomic development of the Thai capital Bangkok and its vicinity. However, groundwater resources are under immense pressure due to population growth, rapid urbanisation, overexploitation, and climate change. Therefore, evaluating the combined impact of climate change and land-use change on groundwater recharge can be useful for developing sound groundwater management systems. In this research, the future climate is projected using three Regional Climate Models (RCMs), namely ACCESS-CSIRO-CCAM, CNRM-CM5-CSIRO-CCAM, and MPI-ESM-LR-CSIRO-CCAM for three future periods: near future (2010-2039), mid future (2040-2069), and far future (2070-2099) under two Representative Concentration Pathway (RCP) scenarios 4.5 and 8.5 as suggested in the IPCC's Fifth Assessment Report. All RCMs project the temperature to rise incessantly, although future precipitation is predicted to fluctuate (increase and decrease) among the various RCMs and RCP scenarios. A Dyna-CLUE model is employed to analyse the future land-use change scenarios (low, medium, and high urbanisation), with the aim of expanding the built-up area and creating land-use maps covering the period to 2099. A hydrological model, WetSpass, is used to estimate groundwater recharge under future climate and land-use change. The findings reveal that groundwater recharge is expected to decrease in high and medium urbanisation areas, ranging from 5.84 to 20.91 mm/yr for the RCP 4.5 scenario and 4.07 to 18.72 mm/yr for RCP 8.5. In contrast, for the low urbanisation scenario, the model projects an increase in groundwater recharge ranging from 7.9 to 16.66 mm/yr for the RCP 4.5 scenario and 5.54 to 20.04 mm/yr for RCP 8.5., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2021 Elsevier B.V. All rights reserved.)

Water crisis across the globe has placed high pressure on social development due to the need to balance the water consumption between sustainable economy and functioning ecosystem. Integrated process-based modeling has been reported as an effective tool to better understand the complex mechanisms of water issues on a basin scale. Considering that it is still relatively difficult to simulate the water quantity-quality processes simultaneously, this study proposed an integrated modeling framework by coupling a hydrological model with a water quality model. Taking the Xiaoqing River Basin in the Shandong Province of northern China as an example, this study coupled a distributed hydrological model, SWAT, with a one-dimensional hydrodynamic-water quality model, HEC-RAS, to investigate its ability to simulate water quality and quality at the basin scale. The coupling of the two models adopted the "output-input" scheme, where the runoff modeling results from SWAT are input into HEC-RAS for hydrodynamic and water quality simulations of the river channel. The results show that the SWAT model can adequately reproduce runoff with accepted accuracy for the calibration and validation periods with acceptable R 2 and Nash-Sutcliffe coefficients for the two hydrological stations. Further analysis also shows that the coupled model can simulate the concentration of ammonia nitrogen (NH 4 -N) and the chemical oxygen demand (COD) in the middle and upper stream of the river for both low and high flow periods. The coupling of the hydrological and hydraulic models in this study provides a good tool for identifying the spatial patterns of the water pollutants over the basin and, thus, helps simplify precision water management., Competing Interests: Declaration of competing interest The authors declare no conflict of interest., (Copyright © 2021 Elsevier B.V. All rights reserved.)

This paper assesses the recently intensified saline water intrusion (SI) and drought in the Vietnamese Mekong Delta (VMD). While the existing literature predominantly points the cause of drought to the hydropower dams in the upstream of the Mekong Basin, we contribute new physical evidence of the intensification of saline water intrusion (through backwater effect) in the VMD caused by three anthropogenic drivers: riverbed incision (due to both riverbed mining and dam construction), sea level rise and land subsidence. Thereupon, we highlight that it is critical to not underestimate the impacts from the localized factors, especially the riverbed-mining which can incise the channel by up to 15 cm/year and amplify the salinity intrusion. Our analysis is based on the extensive sets of hourly-to-daily hydrological time series from 11 gauge stations across the VMD. First, several signs of significantly increased tidal amplification (up to 66%) were revealed through the spectral analysis of the hourly water level data. This trend was further validated through the changes in slopes of the rating curves at the tidal zones, implying the relationships between the shift of the backwater effects on the rivers in VMD and the lowered water levels caused by the riverbed incision. Finally, we introduce a novel approach using the annual incision rates of the riverbed to compare four SI driving factors in terms of their relative contributions to the balance between fresh and saline water in the VMD., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2020 Elsevier B.V. All rights reserved.)

Hydrological model parameters are important during representation of the hydrological characteristics of a watershed. The West Seti River Basin (WSRB), a prominent Himalayan Basin of Nepal, is a major source of fresh water in the western region of the country. We used the Soil and Water Assessment Tool (SWAT) for hydrological modelling and identified the most sensitive hydrological parameters, while the Sequential Uncertainty Fitting (SUFI-2) technique was employed for model calibration. The model was calibrated for the study period (1999-2005) with a three-year warm-up period (1996-1998). Subsequently, it was validated for three years (2006-2008). The results show that the large number of Hydrological Response Units (HRUs) for model simulation took a considerable time, without improving the performance statistics. Importantly, significant improvements were observed during both calibration and validation periods when elevation bands (EBs) were taken into consideration. The p-factor, r-factor, coefficient of determination (R 2 ), Nash-Sutcliffe efficiency (NSE), percent bias (PBIAS), Root mean square error (RMSE)-observations, and standard deviation (STDEV) ratio (RSR) were used to measure the performance between observed and simulated values. The values of p-factor, r-factor, R 2 , NSE, PBIAS, and RSR during the calibration were 0.82, 0.80, 0.84, 0.82, 7.2, and 0.42, respectively, whereas during validation they were 0.79, 0.72, 0.83, 0.82, 11.8, and 0.42, respectively. The calibrated model was then used to assess the anticipated river discharge. This study used four regional climate models (RCMs) for precipitation and six for temperature, together with their arithmetical average as multi-model ensembles (MMEs) under two representative concentration pathways (RCPs). We analysed the changes in precipitation, temperature, and river discharge for three future time frames: Future1 (F1: 2020-2044), Future2 (F2: 2045-2069), and Future3 (F3: 2075-2099) with respect to the baseline (1996-2005). The magnitude of changes varied according to the different climate models and warming scenarios. In general, the MMEs showed slightly increasing precipitation (higher during the F2 period), significantly increasing temperature (continuous rising trend), and moderately increasing river discharge (higher during the F2 period). Information such as the anticipated shift in the flow duration curve may be helpful to stakeholders across different water sectors for effective water resource management in the future. From the modelling perspective, the results show greater significance for EBs than HRUs during the modelling of high mountain basins with SWAT. This take-home message would be useful to hydrologists and other stakeholders in evaluating different scenarios over a short duration, without iteratively spending higher computational time., Competing Interests: Declaration of competing interest No conflict of interest., (Copyright © 2020. Published by Elsevier B.V.)

Among many factors the hydrology of a watershed is mainly influenced by climate and land use change. This study examined the impacts of climate change on water resources and extreme events in the Bang Pakong-Prachin Buri River Basin, Thailand using three different Regional Climate Models (RCMs) ACCESS1-CSIRO-CCAM, CNRM-CM5-CSIRO-CCAM, and MPI-ESM-LR-CSIRO-CCAM under RCP4.5 and RCP8.5 emission scenarios. Soil and Water Assessment Tool (SWAT) was used to simulate the future streamflow and Extreme Value Type I distribution (EVI) was used to analyze the extreme events under projected climate conditions. The result of this study showed an increase in maximum (1.9 °C/3.6 °C) and minimum (1.6 °C/3.3 °C) temperatures under RCP4.5/8.5 at the end of the 21st century. In addition, projected rainfall is expected to decrease up to 6.8% (8.5%) in 2050s and then increase slowly such that the decrement remains 4.2% (11.0%) under RCP4.5 (RCP8.5) at the end of the century. The rainfall pattern is projected to considerably fluctuate, in particular, a shift in long term average annual peak event from September to August is predicted in 2080s under emission scenario RCP4.5 (RCP8.5). On the other hand, the average annual discharge is expected to increase up to 13.5% (2020s) and 7.6% (2050s) under RCP4.5 and RCP8.5 respectively with decreasing trend in low flows and increasing trend in high flows. Further analysis on extreme events; strengthened the results from hydrological modeling with an increase in flow volume for the same return period under changed climate conditions. This raises water resources management issues in the Bang Pakong-Prachin Buri River Basin regarding the frequency of flood and drought events in the future calling for proper policy formulation and implementation., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2020 Elsevier Inc. All rights reserved.)

Ho Chi Minh City (HCMC), Vietnam has undergone tremendous transformation in land-use practices in the past few decades. The groundwater-related issues have also been a major concern in the fast-growing southern city of Vietnam. Quantitative prediction of the impact on groundwater recharge due to changes in the land-use pattern of a watershed is crucial in developing sound groundwater management schemes. This study aims to evaluate the impacts of change in land-use patterns on the quantity of groundwater recharge in HCMC. An empirical land-use projection model (Conversion of Land-use and its Effects, Dyna-CLUE) and a hydrological model (Soil and Water Assessment Tool, SWAT) was used for the study. Three future land-use scenarios of Low Urbanization Scenario (LU), Medium Urbanization Scenario (MU) and High Urbanization Scenario (HU) were developed in Dyna-CLUE focusing on the increase of built-up area to generate land-use maps of HCMC until the year 2100. The land-use maps for all three scenarios were then used in the calibrated hydrological model SWAT to get the future recharge in the near future (2016-2045), mid future (2046-2075) and far future (2076-2100). The recharge was observed to increase in the far future of LU by 10% while reduction of 30% and 52% in annual average recharge was observed in far future of MU and HU respectively. It was, thus, observed that change in built-up area has a significant effect on the groundwater recharge in HCMC., Competing Interests: Declaration of competing interests The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2020 Elsevier Inc. All rights reserved.)

Groundwater resources of Kathmandu Valley in Nepal are under immense pressure from multiple stresses, including climate change. Due to over-extraction, groundwater resources are depleting, leading to social, environmental and economic problems. Climate change might add additional pressure by altering groundwater recharge rates and availability of groundwater. Mapping groundwater resilience to climate change can aid in understanding the dynamics of groundwater systems, facilitating the development of strategies for sustainable groundwater management. Therefore, this study aims to analyse the impact of climate change on groundwater resources and mapping the groundwater resiliency of Kathmandu Valley under different climate change scenarios. The future climate projected using the climate data of RCM's namely ACCESS-CSIRO-CCAM, CNRM-CM5-CSIRO-CCAM and MPI-ESM-LR-CSIRO-CCAM for three future periods: near future (2010-2039), mid future (2040-2069) and far future (2070-2099) under RCP 4.5 and RCP 8.5 scenarios were bias corrected and fed into the Soil and Water Assessment Tool (SWAT), a hydrological model, to estimate future groundwater recharge. The results showed a decrease in groundwater recharge in future ranging from 3.3 to 50.7 mm/yr under RCP 4.5 and 19-102.1 mm/yr under RCP 8.5 scenario. The GMS-MODFLOW model was employed to estimate the future groundwater level of Kathmandu Valley. The model revealed that the groundwater level is expected to decrease in future. Based on the results, a groundwater resiliency map of Kathmandu Valley was developed. The results suggest that groundwater in the northern and southern area of the valley are highly resilient to climate change compared to the central area. The results will be very useful in the formulation and implementation of adaptation strategies to offset the negative impacts of climate change on the groundwater resources of Kathmandu Valley., (Copyright © 2020 Elsevier Inc. All rights reserved.)

Recently, the availability of global landuse and land cover data has made hydrological simulation studies possible in data scarce regions. However, the suitability of these products as inputs in the hydrological modeling has not been investigated in detail. Therefore, this study aims to evaluate the use of global landuse and land cover products to simulate the hydrology of the Upper Yom River Basin located in the Northern part of Thailand. Three types of global landuse and landcover products i.e. ESACCI, MCD12Q1, GLC2000 were compared with the landuse product from Land Development Department (LDD) of Thailand. All of these products were used as input in hydrological model, Soil and Water Assessment Tool (SWAT), and performance of the model was compared to simulate hydrological regime including high flow, low flow and seasonal discharge at the outlet and upstream of the basin. The results show that the performance of the hydrological model in simulating the discharge at the basin outlet is better than in the upstream areas while using all types of landuse and land cover data. The model well simulated the annual discharge, wet-season discharge and base flow while using landuse and land cover products of ESACCI and MCD12Q1. Similarly, the high flow and dry-season discharge is well simulated while using MCD12Q1 landuse and landcover products compared to other three products. The results of this study is useful in selecting landuse and land cover products in simulating hydrology for water resources planning and management in data scarce regions., (Copyright © 2019 Elsevier B.V. All rights reserved.)

The rapid expansion in mining activities is deteriorating the water quality in the Chindwin River of Myanmar. In addition, climate change may also aggravate this situation in future. Therefore, the aim of this study was to establish a connection between hydrology, mining area, heavy metal loading, and climate change in the Chindwin River. The hydrology of the upper Chindwin basin was modelled using SHETRAN hydrological model. Geochemical model PHREEQC was utilised to conduct speciation and saturation indexes modelling along the river in order to quantify the precipitated minerals along the river. Thereafter a regression relationship along with LOADEST model was used to quantify the heavy metal loads. Future climate was projected using four RCM's namely ACCESS1-CSIRO-CCAM, CCSM4-CSIRO-CCAM, CNRM-CM5-CSIRO-CCAM and MPI-ESM-LR-CSIRO-CCAM. Future discharges at water quality monitoring stations were simulated using the averaged ensembles. Finally, the heavy metal loading under future climate scenarios were analysed. Results indicate that climate change is likely to reduce future discharges by 3.4%-36.5% in all stations except in the Mokekalae station which shows 1.3%-9.4% increase in the near future discharges. Also, the projected metal loading under future climate conditions shows a decreasing pattern which is similar to the projected discharge pattern. In both baseline and future climate conditions, the area between stations Naung Po Aung and Uru downstream show the highest load effluent for both arsenic and mercury while the area between stations Uru downstream and Mokekalae show the highest load of iron effluent. Although future heavy metal loadings are expected to decrease, mining activities should be carefully monitored, since they discharge a large amount of toxic heavy metal loadings into the Chindwin River which is also expected to suffer a decrease streamflow in future., (Copyright © 2019 Elsevier Inc. All rights reserved.)

In this study, a physically-based hydrological model, Soil and Water Assessment Tool (SWAT) and three types of Artificial Neural Network (ANN) models were used to simulate daily streamflow, and results were compared with observed data for performance analysis. The study was carried out in three different river basins with three different climatic characteristics, namely the West-Seti River Basin in a sub-tropical (partially wet) climatic region, Sre Pok River Basin in a tropical (wet) climatic region and Hari Rod River Basin in a semi-arid (dry) climatic region. The SWAT and ANN models were evaluated using statistical indicators such as the correlation coefficient (R 2 ), Nash-Sutcliffe efficiency (NSE), and percentage bias (PBIAS). The performance of ANN models was found to be very good with both R 2 and NSE values greater than 0.95 for the training and validation periods in the West-Seti River Basin and Sre Pok River Basin. Whereas, in the Hari Rod River Basin, the performance of the SWAT model was good with both R 2 and NSE values greater than 0.60 for the calibration and validation periods. Moreover, the performance of SWAT and ANN models was evaluated based on hydrological indicators (i.e. annual discharge, base flow, Q dry , and Q wet ), during different flow periods (very high to very low flow) using flow duration curves (FDCs). The SWAT model was found to be better for low flow simulation and the ANN model performed better for high flow simulation in the three river basins., (Copyright © 2019 Elsevier B.V. All rights reserved.)

Baiyangdian Lake, the largest shallow lake in the North China Plain, is essential for maintaining ecosystem functioning in this highly populated region. To explore the influences of human activities on the lake's water quality, an improved Water Quality Index (WQI) method and multivariate statistical techniques were adopted to assess the temporal and spatial variations of the lake's water quality and explore the dominant factors of these variations. Datasets for 11 water quality parameters from six monitoring stations were used to evaluate the period spanning from 2006 to 2016. Assessment of the annual WQI showed that the water quality of the lake has generally improved over the past decade. Cluster analysis divided 12 months into the dry and wet periods and the six monitoring stations into those located in the western and eastern parts of the lake. Discriminant analysis demonstrated that with only two parameters (water temperature and fluoride) and six parameters (dissolved oxygen, ammonia nitrogen, total nitrogen, total phosphorus, anionic surfactant, and fecal coliform), 96.0% and 93.8% of the water quality data can be classified into the correct spatial and temporal clusters, respectively. For the principal component analysis and factor analysis, the varifactors detected for the two temporal clusters were similar, and varifactors related to pollution explained more variance in the water quality variation than the ones representing natural factors. For the two spatial clusters, the varifactors were different, indicating they are influenced by different types of anthropogenic activities. Correlation analysis between lake water level and water quality indicated that environmental water allocation to the lake generally improve water quality. These findings provide a more thorough understanding of driving mechanism of water quality and may be helpful for making environmental management decisions in Baiyangdian Lake and other large, shallow lakes in highly populated dryland regions., (Copyright © 2019 Elsevier B.V. All rights reserved.)

Ecological water use efficiency (WUE) is a crucial indicator of hydrological and vegetation carbon cycle coupling and can drastically affect water and energy exchanges. However, little is known about the sensitivity of WUE to water-energy exchange in semiarid regions. Moreover, few studies have considered the link between WUE and water-energy exchange models, i.e., the Budyko-type framework. Here, we investigated the relationships between WUE and a Budyko-type model in a semiarid basin. Seven primary indicators were selected to represent the water, energy, and vegetation variations in the ecosystem: gross primary productivity (GPP), air temperature (T), potential evapotranspiration (PET), evapotranspiration (ET), precipitation (P), soil water content (SWC), and snow water equivalent (SWE). Three similar ecological WUEs were defined as GPP/ET, GPP/P, and GPP/SWC to analyze the factors of the water-energy exchange model (i.e., ET/P, PET/P, SWC/P, and SWE/P). Based on the results, four linear Budyko-type models were proposed for the basin (GPP/P, ET/P, SWC/P, and SWE/P as a function of PET/P). The results suggested that both SWC and P influenced the trend of GPP variation, whereas P influenced the lower limit of GPP or GPP/P within the Budyko model for grassland areas. The results indicated that the rate of increase of SWC was lower than that of P in forest areas because of differences in canopy structure. The results also revealed a nonlinear (s-type) relationship between the WUEs and the underlying surface parameter m within the Budyko framework, suggesting that unit plant productivity consumes less water when the water-energy supply condition is invariant, if the variation of the underlying surface characteristics promotes the increase of m. Our research provides new insight regarding quantification of the sensitivity of WUE to the water-energy balance in a semiarid region., (Copyright © 2019 Elsevier B.V. All rights reserved.)

Sustainable management strategies for water resources rely on accurate knowledge about the dynamics of hydrological processes, especially in drylands, where freshwater is the limiting factor for the development of human society and ecosystems. The populated Loess Plateau (LP) in North China is a typical semi-arid region where competition for water between people and nature is worth noting because of afforestation promoted by the Grain to Green Program. In this study, changes in key components of terrestrial water storage (TWS) in the LP were explored using a multi-satellite approach, including Gravity Recovery and Climate Experiment (GRACE) observations and Earth observations of precipitation, evapotranspiration and soil moisture. By integrating data on human water use from different sources with satellite observations, we were able to examine the mechanisms driving these changes. The results demonstrated that, according to an evaluation based on reproducing TWS computed from the regional water balance in the LP, the mascon solution of the Center for Space Research (CSR) at University of Texas at Austin performed best out of the commonly used GRACE products. Regional TWS derived from the CSR mascon solution in the LP decreased significantly for the period 2003-2015. Significant decreases were also detected for regional ground water storage (GWS) estimated by decomposing the GRACE TWS using multi-sources remote sensing data. GWS made the greatest contribution to the changes in TWS. Increased plant transpiration was one reason for the decreasing trend of GWS. Because changes in precipitation, soil moisture and water consumed by irrigation were minor at regional scales, we concluded that the increase of transpiration is driven by deep-rooted trees planted, which use the part of precipitation that previously recharged groundwater. The findings from this study are valuable for water resource management and ecological restoration in semi-arid regions with high populations., (Copyright © 2019 Elsevier B.V. All rights reserved.)

This paper addresses the problem of missing latent time series information caused by the differences in the analysis of time series data and non-time series data. A time series trend structure model (TSTM) was established using the analysis of time series patterns and rules, the trends of patterns and rules, and trends in confidence and support. Shandong Province was selected as the study area. Rainfall and evaporation time series data from this area were input into the TSTM. The results show that: (1) the structure of multi-year precipitation and evaporation trends of the meteorological stations in the study area have continuously increasing or decreasing characteristics. The TSTM can excavate the different trend structure characteristics of different meteorological elements and enables diversity in time series data analysis; (2) the evaporation trend structure tends to change synchronously with increases and decreases in precipitation and evaporation. The synchronous change frequency is essentially the same as that of the rainfall trend structure. This indicates that the TSTM has spatial and temporal characteristics for time series data analysis; and (3) from the maximal non-descending and non-ascending subsequence in the TSTM, it can be concluded that there exists continuity in the years when the trend structure of precipitation and evaporation increases and decreases synchronously. In addition, the degree of similarity in the model is well reflected in the spatial distribution characteristics of time series data, and the model provides clustering characteristics for time series data analysis. The TSTM proposed in this paper can effectively obtain the potential hydrological information contained in time series data, and provides a scientific and reliable basis for rules for the spatial optimization of watershed data and for the calibration of hydrological models., (Copyright © 2019 Elsevier B.V. All rights reserved.)

Point sources are important routes through which pollutants enter rivers. It is important to identify the characteristics of and trace the origins of water pollutants. In this study, an artificial intelligence system called the integrated long short-term memory network (LSTM), using cross-correlation and association rules (Apriori), was used to identify the characteristics of water pollutants and trace industrial point sources of pollutants. Water quality monitoring data from Shandong Province, China, were used to verify the applicability of the artificial intelligence system using a cross-correlation method to develop a water quality cross-correlation map. The map was used to identify highly correlated pollutants affecting water quality, then the association rules (Apriori) were used to track the pollutants to industries common in the study area. The highly correlated water pollutants and relevant industries were used as inputs for the LSTM to determine how well the LSTM traced sources of water pollutants. The results showed that (1) changes in water quality were affected in different ways by different industries and different distributions and production cycles of the pollutant point sources; (2) water quality correlation maps can be used to identify regular and abnormal fluctuations in point source pollutant emissions by identifying changes in water quality characteristics and frequent itemsets in water quality indices can be used to trace the industries that most strongly affect water quality; and (3) the LSTM accurately traced point sources of future changes in water quality. In conclusion, the artificial intelligence scheme described here can be applied to aquatic systems., (Copyright © 2019 Elsevier B.V. All rights reserved.)

Soil water content (SWC) of a vertical profile plays an important role in the soil-plant-atmosphere continuum system through eco-hydrological process, which was controlled by multiple factors. Previous studies ignored soil water from a systematic perspective because of the lack of suitable methods to deal with interrelated factors. We developed a meta-model based on structural equation model (SEM) to identify the factors contributing to soil water, and the interactions among these factors, in a semi-arid grassland system. The model was based on the hypothesis that soil water is affected by hydrological variables (precipitation: P, evapotranspiration: E and underground water: GW), vegetation (vegetation coverage: VC and above ground biomass: AGB), and soil parameters (soil organic matter: SOM and bulk density: BD). E and AGB decrease soil water content, while VC and SOM help to retain soil water content. The proportion of explained variation in soil water increased with depth due to increasing stability. The most important contributors were AGB (r∂ = -0.15) and VC (r∂ = 0.39), and their contributions were opposite because their mechanisms differed. The accumulation of AGB in the growth season consumed soil water through root uptake. The contribution of AGB increased with depth, inferring that grassland species are xerophytes with deep roots to access soil water during drought. Coverage positively contributed to soil water, but its influence decreased with depth because its main effects (intercepting rainfall and providing shade) were at the surface. This systematic perspective of how hydrological, vegetation, and soil properties affect soil water will be useful to guide the management of semi-arid grasslands., (Copyright © 2019 Elsevier B.V. All rights reserved.)

This study investigates rice yield and evaluates potential adaptation measures on field management practices for rainfed rice production under climate change scenarios in the Songkhram River Basin, Thailand. The top-down and bottom-up approaches are combined to evaluate the future climate conditions in the Songkhram River Basin and identify adaptation strategies respectively. An ensemble of four Regional Climate Models (RCMs) bias-corrected using the Quantile Mapping technique was used to project the future climate under two climate change scenarios (RCP4.5 and RCP8.5). The DSSAT crop simulation model was used to simulate rice yield and evaluate the impacts of climate change on rice yield, as well as the feasibility of four adaptation options, which were solicited from four hundred farmers through questionnaire surveys in the basin. The strategies include (i) change in planting date, (ii) change in fertiliser application date, (iii) change in fertiliser application dose, and (iv) supplying irrigation water. Based on the model results, future maximum and minimum temperatures are expected to increase by 2.8 and 3.2 °C respectively under RCP8.5 scenario for 2080s. Although annual rainfall may be unchanged, rainfall patterns will shift earlier in future. Evaluation of adaptation strategies suggest that supplying irrigation water under RCP4.5 and RCP8.5 scenarios respectively are the best strategies to increase rice yield under climate change scenarios. Change in fertiliser application date and change in planting date can increase the future rice yield by 12 and 8%, respectively under RCP4.5 scenario for 2080s. Adjusting the fertiliser application dose may however reduce future rice yield. Although supplying irrigation water can aid the production of rainfed rice, other concerns such as the source of water are involved. The feasibility of adaptation actions would depend largely on available resources and mindset of farmers. Further work is warranted in exploring a combination of adaptation strategies and management plans to combat the adverse impacts of climate change., (Copyright © 2018 Elsevier B.V. All rights reserved.)

The livelihoods of people in the Songkhram River Basin (SRB) rely heavily on river water, especially those involved in the fisheries. Extensive landuse changes in SRB have raised concerns regarding its impacts in water quality in addition to the looming climate change effects. More importantly there is lack of detailed studies on landuse and climate change impact on river water quality for the sustainable water management. Therefore, this study aimed at quantifying the individual and integrated impact of climate and landuse change in streamflows and nitrate nitrogen loadings in the Songkhram River. Future streamflow was simulated using Soil and Water Assessment Tool (SWAT) whereas future climate projections were derived from three Regional Climate Models (RCMs) under two Representative Concentration Pathways (RCPs). A dynamic, spatially explicit, land use change model (Dyna-CLUE) was used to generate two future land use change scenarios namely economic and conservation scenarios. The results show that in future climate change is to be responsible for a 19.5 and 24% decrease in streamflow and 11.25 and 15.25% decrease in nitrate nitrogen loading as projected under RCP 4.5 and 8.5 scenarios, respectively. Whereas landuse change is found to be responsible for a 5.30 and 6.35% increase in streamflow and an 11 and 11.60% decrease in nitrate nitrogen loading under economic and conservation scenarios, respectively in future. Thus, in conjunction, these two agents are seen to play against each other, creating a net effect of decreased future streamflow (16% on average); whereas, in the case of nitrate nitrogen loading, the two work in tandem, leading to an amplified reduction (24.50% on average). Although landuse change is found to be the less afflicting agent, its role should not be overlooked-it may very well further exacerbate the situation if there is a greater unfavourable climatic variation than projected., (Copyright © 2018 Elsevier B.V. All rights reserved.)

Impacts of climate change on water resources, especially groundwater, can no longer be hidden. These impacts are further exacerbated under the integrated influence of climate variability, climate change and anthropogenic activities. The degree of impact varies according to geographical location and other factors leading systems and regions towards different levels of vulnerability. In the recent past, several attempts have been made in various regions across the globe to quantify the impacts and consequences of climate and non-climate factors in terms of vulnerability to groundwater resources. Firstly, this paper provides a structured review of the available literature, aiming to critically analyse and highlight the limitations and knowledge gaps involved in vulnerability (of groundwater to climate change) assessment methodologies. The effects of indicator choice and the importance of including composite indicators are then emphasised. A new integrated approach for the assessment of groundwater vulnerability to climate change is proposed to successfully address those limitations. This review concludes that the choice of indicator has a significant role in defining the reliability of computed results. The effect of an individual indicator is also apparent but the consideration of a combination (variety) of indicators may give more realistic results. Therefore, in future, depending upon the local conditions and scale of the study, indicators from various groups should be chosen. Furthermore, there are various assumptions involved in previous methodologies, which limit their scope by introducing uncertainty in the calculated results. These limitations can be overcome by implementing the proposed approach., (Copyright © 2017 Elsevier B.V. All rights reserved.)

Northeast Thailand makes a significant contribution to fragrant and high-quality rice consumed within Thailand and exported to other countries. The majority of rice is produced in rainfed conditions while irrigation water is supplied to rice growers in the dry season. This paper quantifies the potential impact of climate change on the water footprint of rice production using the DSSAT (CERES-Rice) crop growth model for the Nam Oon Irrigation Project located in Northeast Thailand. Crop phenology data was obtained from field experiments and used to set up and validate the CERES-Rice model. The present and future water footprint of rice, the amount of water evaporated during the growing period, was calculated under current and future climatic condition for the irrigation project area. The outputs of three regional climate models (ACCESS-CSIRO-CCAM, CNRM-CM5-CSIRO-CCAM, and MPI-ESM-LR-CSIRO-CCAM) for scenarios RCP 4.5 and RCP 8.5 were downscaled using quantile mapping method. Simulation results show a considerably high increase in the water footprint of KDML-105 and RD-6 rice varieties ranging from 56.5 to 92.2% and 27.5 to 29.7%. respectively for the future period under RCP 4.5, and 71.4 to 76.5% and 27.9 to 37.6%, respectively under RCP 8.5 relative to the simulated baseline water footprint for the period 1976-2005. Conversely, the ChaiNat-1 variety shows a decrease in projected water footprint of 42.1 to 39.4% under RCP 4.5 and 38.5 to 31.7% under RCP 8.5. The results also indicate a huge increase in the future blue water footprint, which will consequently cause a high increment in the irrigation water requirement in order to meet the plant's evaporation demand. The research outcome highlights the importance of proper adaptation strategies to reduce or maintain acceptable water footprints under future climate conditions., (Copyright © 2017 Elsevier B.V. All rights reserved.)

Assessment of the climate and land-use change impacts on the hydrology and water quality of a river basin is important for the development and management of water resources in the future. The objective of this study was to examine the impact of climate and land-use change on the hydrological regime and nutrient yield from the 3S River Basin (Sekong, Srepok, and Sesan) into the 3S River system in Southeast Asia. The 3S Rivers are important tributaries of the Lower Mekong River, accounting for 16% of its annual flow. This transboundary basin supports the livelihoods of nearly 3.5 million people in the countries of Laos, Vietnam, and Cambodia. To reach a better understanding of the process and fate of pollution (nutrient yield) as well as the hydrological regime, the Soil and Water Assessment Tool (SWAT) was used to simulate water quality and discharge in the 3S River Basin. Future scenarios were developed for three future periods: 2030s (2015-2039), 2060s (2045-2069), and 2090s (2075-2099), using an ensemble of five GCMs (General Circulation Model) simulations: (HadGEM2-AO, CanESM2, IPSL-CM5A-LR, CNRM-CM5, and MPI-ESM-MR), driven by the climate projection for RCPs (Representative Concentration Pathways): RCP4.5 (medium emission) and RCP8.5 (high emission) scenarios, and two land-use change scenarios. The results indicated that the climate in the study area would generally become warmer and wetter under both emission scenarios. Discharge and nutrient yield is predicted to increase in the wet season and decrease in the dry. Overall, the annual discharge and nutrient yield is projected to increase throughout the twenty-first century, suggesting sensitivity in the 3S River Basin to climate and land-use change. The results of this study can assist water resources managers and planners in developing water management strategies for uncertain climate change scenarios in the 3S River Basin., (Copyright © 2016 Elsevier B.V. All rights reserved.)

This study aimed at evaluating three index-overlay methods of vulnerability assessment (i.e., DRASTIC, GOD, and SI) for estimating risk to pollution of shallow groundwater aquifer in the Kathmandu Valley, Nepal. The Groundwater Risk Assessment Model (GRAM) model was used to compute the risk to groundwater pollution. Results showed that DRASTIC and SI methods are comparable for vulnerability assessment as both methods delineate around 80% of the groundwater basin area under high vulnerable zone. From the perspective of risk to pollution results, DRASTIC and GOD methods are comparable. Nevertheless, all the three methods estimate that at least 60% of the groundwater basin is under moderate risk to NO 3 -N pollution, which goes up to 75% if DRASTIC or GOD-based vulnerabilities are considered as exposure pathways. Finally, based on strength and significance of correlation between the estimated risk and observed NO 3 -N concentrations, it was found that SI method is a better-suited one to assess the vulnerability and risk to groundwater pollution in the study area. Findings from this study are useful to design strategies and actions aimed to prevent nitrate pollution in groundwater of Kathmandu Valley in Nepal., (Copyright © 2016 Elsevier B.V. All rights reserved.)

Groundwater vulnerability and risk assessment is a useful tool for groundwater pollution prevention and control. In this study, GIS based DRASTIC model have been used to assess intrinsic aquifer vulnerability to pollution whereas Groundwater Risk Assessment Model (GRAM) was used to assess the risk to groundwater pollution in the groundwater basin of Kathmandu Valley. Seven hydrogeological factors were used in DRASTIC model to produce DRASTIC Index (DI) map which represent intrinsic groundwater vulnerability to pollution of the area. The seven hydrogeological factors used were depth to water, net recharge, aquifer media, soil media, topography, impact of vadose zone, and hydraulic conductivity of aquifer. GIS based GRAM was used to produce likelihood of release of hazards, likelihood of detection of hazards, consequence of hazards and residual risk of groundwater contamination in terms of nitrate in the groundwater basin. It was found that more than 50% of the groundwater basin area in the valley is susceptible to groundwater pollution and these areas are mostly in Northern groundwater district Low and very low vulnerable areas account for only 13% and are located in Central and Southern groundwater districts. However after taking into account the barriers to groundwater pollution and likelihood of hazards release and detection, it was observed that most areas i.e. about 87% of the groundwater basin are at moderate residual risk to groundwater pollution. The resultant groundwater vulnerability and risk map provides a basis for policy makers and planner's ability to use information effectively for decision making at protecting the groundwater from pollutants., (Copyright © 2016 Elsevier B.V. All rights reserved.)